Affiliation:
1. China University of Geosciences, School of Mathematics and Physics, Wuhan 430074, China.(corresponding author); .
2. China University of Geosciences, Institute of Geophysics and Geomatics, Wuhan 430074, China..
Abstract
Deep-learning (DL) technology has emerged as a new approach for seismic data interpolation. DL-based methods can automatically learn the mapping between regularly subsampled and complete data from a large training data set. Subsequently, the trained network can be used to directly interpolate new data. Therefore, compared with traditional methods, DL-based methods reduce the manual workload and render the interpolation process efficient and automatic by avoiding the selection of hyperparameters. However, two limitations of DL-based approaches exist. First, the generalization performance of the neural network is inadequate when processing new data with a different structure compared to the training data. Second, the interpretation of the trained networks is very difficult. To overcome these limitations, we have combined the deep neural network and classic prediction-error filter (PEF) methods, proposing a novel seismic data dealiased interpolation framework called prediction-error filters network (PEFNet). The PEFNet designs convolutional neural networks to learn the relationship between the subsampled data and the PEFs. Thus, the filters estimated by the trained network are used for the recovery of missing traces. The learning of filters enables the network to better extract the local dip of seismic data and has a good generalization ability. In addition, PEFNet has the same interpretability as traditional PEF-based methods. The applicability and the effectiveness of our method are demonstrated here by synthetic and field data examples.
Funder
National Key R&D Program of China
Fundamental Research Funds for the Central Universities
Hubei Subsurface Multi-scale Imaging Key Laboratory
Science and Technology Research Project of Hubei Provincial Department of Education
Publisher
Society of Exploration Geophysicists
Subject
Geochemistry and Petrology,Geophysics
Cited by
19 articles.
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